Multiple Imputation Using Weighted Quantile Sum Regression
The miWQS package handles the uncertainty due to below the detection limit in a correlated component mixture problem. Researchers want to determine if a set/mixture of continuous and correlated components/chemicals is associated with an outcome and if so, which components are important in that mixture. These components share a common outcome but are interval-censored between zero and low thresholds, or detection limits, that may be different across the components. This package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes (Hargarten & Wheeler (2020) <10.1016>). The imputation models are: bootstrapping imputation (Lubin et.al (2004) <10.1289>), univariate Bayesian imputation (Hargarten & Wheeler (2020) <10.1016>), and multivariate Bayesian regression imputation.10.1016>10.1289>10.1016>
*First Release of Package to the public.
*For updates to CRAN team, see cran-comments.
- Replaced examples using example dataset in package instead of using package wqs. Looks cleaner
- Remove printed output from estimate.wqs.
- Made documentation from estimate.wqs clearer.
- Cleaned up print.wqs documentation
- Reworked plot.wqs() function by using ggplot2 instead of base plotting in R.
- Fixed bug in doing Poisson Rate WQS regressions. Added argument offset to the check_function() and randomize.train()
- For updates to CRAN team, see cran-comments.
- Added a
NEWS.md file to track changes to the package.
- First Release of the Package to CRAN team
- Successfully passed windows check.